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Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
[article]
2016
arXiv
pre-print
A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing interpretable dynamics from neural trajectories. Our model assumes low-dimensional continuous dynamics in a finite volume. It incorporates a prior assumption about globally contractional dynamics to avoid overly enthusiastic extrapolation outside of the support of observed trajectories. We show that our model can
arXiv:1608.06546v2
fatcat:vtiwcuvw25ftbcpgtnlvyeqkhe